– Introduction

The ambition of the GPW13 is to improve the health of billions of people in the next five years. The WHO Impact Measurement Framework is part of GPW13. It measures progress at three levels:

  1. 46 outcome indicators and their global targets for 2023, covering a range of health issues
  2. The Triple Billion targets, to be achieved by 2023:
    • billion more people benefiting from universal health coverage
    • billion more people better protected from health emergencies
    • billion more people enjoying better health and well-being.
  3. Healthy life expectancy (HALE), quantifying expected years of life in good health as a measure of the overall health of populations.

The purpose of this document is to be a live document that summarises the data “in hand” for the 46 outcome indicators i.e. the data that have been submitted to the DDI via the Global Health Observatory (GHO) or through other means. Based on either the estimates as generated by technical programmes or according to a set of estimation/extrapolation methodologies (with statistical assumptions and R-code provided), the document explores what estimates of baseline and projection values can be determined for each of the 46 indicators using the data we have and also highlights the indicators for which data gaps restrict robust estimation and extrapolation.

Detailed information on the methods of the GPW13 is available for download in the methods report which can be obtained from here,


– Table of indicators

The 46 outcome indicators are listed in the table below and can be downloaded from here. For each of these indicators, the ones highlighted in orange are included in the corresponding billions calculation whereas the ones highlighted in green are relevant to the billions but not directly included in the calculation.

GHO code Indicator UHC HEP HPOP
  1. SDG 1.5.1
SDGDISASTER Number of persons affected by disasters (per 100 000 population) Relevant
  1. SDG 1.a.2
GHED_GGHE-DGGE_SHA2011 Domestic general government health expenditure (GGHE-D) (% of general government expenditure (GGE)) Relevant
  1. SDG 2.2.1
NUTRITION_HA_2 Prevalence of stunting in children under 5 (%) Included
  1. SDG 2.2.2
NUTRITION_WH_2 Prevalence of wasting in children under 5 (%) Included
  1. SDG 2.2.2
NUTRITION_WH2 Prevalence of overweight in children under 5 (%) Included
  1. SDG 3.1.1
MDG_0000000026 Maternal mortality ratio (per 100 000 live births) Relevant
  1. SDG 3.1.2
MDG_0000000025 Proportion of births attended by skilled health personnel (%) Relevant
  1. SDG 3.2.1
MDG_0000000007 Under-five mortality rate (per 1000 live births) Relevant
  1. SDG 3.2.2
WHOSIS_000003 Neonatal mortality rate (per 1000 live births) Relevant
  1. SDG 3.3.1
SDGHIV New HIV infections (per 1000 uninfected population) Relevant
  1. SDG 3.3.2
MDG_0000000020 Tuberculosis incidence (per 100 000 population) Relevant
  1. SDG 3.3.3
SDGMALARIA Malaria incidence (per 1000 population at risk) Relevant
  1. SDG 3.3.4
SDGHEPHBSAGPRV Hepatitis B incidence (measured by: surface antigen (HBsAg) prevalence among children under 5 years) Relevant
  1. SDG 3.3.5
SDGNTDTREATMENT Number of people requiring interventions against NTDs Relevant
  1. SDG 3.4.1
NCDMORT3070 Probability of dying from any of CVD, cancer, diabetes, CRD (aged 30–70) (%) Relevant
  1. SDG 3.4.2
SDGSUICIDE Suicide mortality rate (per 100 000 population) Included
  1. SDG 3.5.1
Coverage of treatment interventions for substance-use disorders (%) Relevant
  1. SDG 3.5.2
SA_0000001688 Total alcohol per capita consumption in adults aged 15+ (litres of pure alcohol) Included
  1. SDG 3.6.1
RS_198 Road traffic mortality rate (per 100 000 population) Included
  1. SDG 3.7.1
SDGFPALL Proportion of women (aged 15–49) having need for family planning satisfied with modern methods (%) Included
  1. SDG 3.8.1
UHC_INDEX_REPORTED UHC Service Coverage Index Included
  1. SDG 3.8.2
FINPROTECTION_CATA_TOT_10_POP Population with household expenditures on health >10% of total household expenditure or income (%) Included
  1. SDG 3.9.1
SDGAIRBODA Mortality rate attributed to air pollution (per 100 000 population) Relevant
  1. SDG 3.9.2
SDGWSHBOD Mortality rate attributed to exposure to unsafe WASH services (per 100 000 population) Relevant
  1. SDG 3.9.3
SDGPOISON Mortality rate from unintentional poisoning (per 100 000 population) Relevant
  1. SDG 3.a.1*
SDGTOBACCO Prevalence of tobacco use in adults aged 15+ (%) Included Included
  1. SDG 3.b.1
WHS4_100 Proportion of population covered by all vaccines included in national programmes (DTP3, MCV2, PCV3) (%) Included Relevant
  1. SDG 3.b.3
SDGHEALTHFACILITIESESSENTIALMEDS Proportion of health facilities with essential medicines available and affordable on a sustainable basis (%) Relevant
  1. SDG 3.c.1a
HWF_0001 Density of health workers (doctors; nurse and midwives; pharmacists; dentists per 10 000 population) Included
  1. SDG 3.d.1
SDGIHR2018 International Health Regulations (IHR) capacity and health emergency preparedness Included Included
  1. SDG 3.d.2
Proportion of bloodstream infections due to antimicrobial resistant organisms (%) Relevant
  1. SDG 4.2.1
SE_DEV_ONTRK Proportion of children under 5 developmentally on track (health, learning and psychosocial well-being) (%) Included
  1. SDG 5.2.1
SDGIPV Proportion of women (aged 15–49) subjected to violence by current or former intimate partner (%) Included
  1. SDG 5.6.1
SG_DMK_SRCR_FN_ZS Proportion of women (aged 15–49) who make their own decisions regarding sexual relations, contraceptive use and reproductive health care (%) Relevant
  1. SDG 6.1.1
WSH_WATER_SAFELY_MANAGED Proportion of population using safely managed drinking water services (%) Included
  1. SDG 6.2.1b
WSH_SANITATION_SAFELY_MANAGED Proportion of population using safely managed sanitation services and hand-washing facility (%) Included
  1. SDG 7.1.2
SDGPOLLUTINGFUELS Proportion of population with primary reliance on clean fuels (%) Included
  1. SDG 11.6.2
SDGPM25 Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (µg/m3) Included
  1. SDG 16.2.1
Proportion of children (aged 1–17) experiencing physical or psychological aggression (%) Included
  1. Health Emergencies (vaccines)
Vaccine coverage for epidemic prone diseases Included
  1. Health Emergencies (fragile)
Proportion of vulnerable people in fragile settings provided with essential health services (%) Included
  1. WHA66.10 (BP)
BP_04 Prevalence of raised blood pressure in adults aged 18+ Included
  1. WHA66.10 (TFA)
NCD_CCS_SatFat Effective policy/regulation for industrially produced transfatty acids (TFA) (Y/N) Included
  1. WHA66.10c
NCD_BMI_30C Prevalence of obesity (%) Included
  1. WHA68.3
WHS3_49 Number of cases of poliomyelitis caused by wild poliovirus (WPV) Relevant
  1. WHA68.7
Patterns of antibiotic consumption at national level Relevant
Note:
a There are separate indicators for doctors, nurses, midwives and pharmacists in GHO, weighted average?
b Sanitation and hand-washing have separate indicators in GHO, weighted average?
c Adults and children have separate indicators in GHO, weighted average?
* For the HPOP Billion non-age-standardized data are used.
SDG 1.a.2 includes several indicators. Only the one relating to health expenditure is used in GPW13.

The 6 indicators without a gho_code in the table above are currently not in the GHO. The first do not have any data at all as yet nor do they have any additional info or focal points leading i.e.

  • Coverage of treatment interventions for substance-use disorders (%) [UHC, SDG 3.5.1]
  • Proportion of bloodstream infections due to antimicrobial resistant organisms (%) [UHC, SDG 3.d.2]
  • Patterns of antibiotic consumption at national level [UHC, WHA 68.7]

The remaining three are components of the billions calculation and do not currently have data in GHO (need to follow up as to where they are and length of time-series) i.e.

  • Proportion of children (aged 1–17) experiencing physical or psychological aggression (%) [HPOP, SDG 16.2.1]
  • Vaccine coverage for epidemic prone diseases [HEP]
  • Proportion of vulnerable people in fragile settings provided with essential health services (%) [HEP]

The remaining 40 indicators that are in the GHO can be extracted using the ghost R-package which was designed to provide a simple interface for extracting data from the World Health Organization’s Global Health Observatory (GHO) database using the GHO Open Data Protocol API. The package allows for exploration of indicators and dimensions available in the GHO and extract of these into R data frames.


– Methods for infilling and projections


Statistical models

Generalized Linear Models (GLMs)

A GLM has the structure \[ g\big(\mu_i\big) = \boldsymbol{x}_i \boldsymbol{\beta}, \] where \(\mu_i\) is the expectation of response variable \(Y_i\) such that \(\mu_i \equiv \mathbb{E}\big(Y_i\big)\), \(g\) is a link function, \(\boldsymbol{x}_i\) is the \(i\)th row of the covariate matrix \(\boldsymbol{X}\), and \(\boldsymbol{\beta}\) is a vector of unknown coefficients. Akin to linear models, GLMs center around a linear predictor, \(\boldsymbol{X}\boldsymbol{\beta}\), and allow for link functions other than the identity function. GLMs also allow for any distribution from the exponential family to model the dependent variable instead of just assuming it Gaussian. The exponential family consists of distributions whose probability density function can be written as \[ f_{\theta}\big(y\big) = \exp \bigg( \frac{y \theta - b(\theta)}{a(\Phi)} + c(y, \Phi)\bigg), \] where \(a\), \(b\), and \(c\) are arbitrary functions, \(\Phi\) is a scale parameter, and \(\theta\) is the canonical parameter of the distribution.

For the projections or infilling univariate models by country and indicator are used.

Generalized Additive Models (GAMS)

Building upon GLMs, a GAM has the structure:

\[ g\big(\mu_i\big) = \boldsymbol{x}_i^* \boldsymbol{\theta} + f_{1}\big(x_{1, i}\big) + f_{2}\big(x_{2, i}\big) + f_{3}\big(x_{3, i}, x_{4, i}\big) + \ldots, \]

where \(\mu_i\) again is the expectation of response variable \(Y_i\), i.e., \(\mu_i \equiv \mathbb{E}\big(Y_i\big)\), the response variable \(Y_i\) follows any distribution from the exponential family, parametric model components have model matrices with rows \(\boldsymbol{x}_i^*\), the vector \(\boldsymbol{\theta}\) is of the corresponding parameters, and covariates \(x_k\) are smoothed with the functions \(f_j\).

The smooth functions are non-parametric estimates of the actual functional relationship between the response variable and the predictor variable to which the smooth function is applied. Unlike LMs or GLMs, a smooth function is non-parametric, i.e., it does not assume the response variable to follow any specific distribution. As an explanatory example, consider a model containing one smooth function of one predictor variable with the identity function as the link function,

\[ y_i = f\big(x_i\big) + \varepsilon_i, \]

where \(y_i\) is a response variable, \(x_i\) is a predictor variable, \(f\) is a smooth function, and the \(\varepsilon_i\) follow the normal distribution with a mean of zero and a variance of \(\sigma^2\). GAMS can also be used for time-series modelling with the form \[y_t = f(log(t)) + \varepsilon_{t}\] where \(y_t\) is the time-series variable to be forecasted using time \(t\). The smoothing function can be on \(t\) or on \(log(t)\) to determine moderate forecasts. The model can be assumed to have autoregressive lag-1 (AR-1) errors:
\[\varepsilon_{t} =\psi_1 \varepsilon_{t-1} + w_t\] where \(w_t \sim \text{ iid }N(0,\sigma^2)\). The \(\psi_1\) correlation is estimated and accounted for in the parameter estimation for the smoother \(f(\cdot)\).

For the projections or infilling univariate models by country and indicator are used.

GBoostM

GBoostM is shorthand for Gradient Boosting Machines. GBoostM is one of the most powerful learning ideas introduced in the last twenty years. It is a machine learning technique for regression and classification problems which produces a prediction model in the form of an ensemble of weak prediction models, building the final model in an iterative fashion.

The purpose of boosting is to sequentially apply the weak classification algorithm to repeatedly modified versions of the data, thereby producing a sequence of weak classifiers \(G_m(x)\), \(m = 1, 2, ... , M\) that together reduce the error in the prediction.

The final model finds the correct mathematical manipulations to turn the input into the output. For the projection and infilling, the GBoostM works on the entire pooled indicator dataset across all countries.

Fixed logit

Booth conducted a review of forecasting methods related to health and specifically mortality and grouped them into three broad categories. The first group comprises of the extrapolation methods which make use of the regularity of trends over time. A second group is made up of the explanation methods which utilize structural or epidemiological models and covariates in the causal pathway to forecast. A third group can be defined as expectation, for which forecasts are based on the opinions of experts. The result is generally a fixed rate of increase or decrease for the forecasted measure based on the information available to the expert. The challenge with this approach is the subjectivity of either the direction or magnitude of the change moving forward, which may not have reproducible reasoning to support validity.

A crude assumption can be made to project indicators under the expectation umbrella by assuming that the rate of change in the indicators for the next few years is very similar to the average logit change in the most recent years and that this change is not expected to persist with the same intensity but rather to diminish with time.

Average

The series labelled Average gives the final projection estimate. It takes the average of the various statistical models used for the particular country-year-indicator combination. If only a GAM model is used, for example, then the Average is equivalent to the mean GAM whereas if both GLM and GAM are used, then the Average is calculated using the means of the GAM and GLM series. In general the simple heuristic applied for the projection is that if there is only a single data point for the indicator-country, then we turn to the GBoostM to complete the series. As the boosting pools all the data it is sometimes less accurate in-sample, as compared to the GAM, GLM and Fixed logit difference - which work specifically on a single country time-series. If there are at least two data points, we can calculate the Fixed difference, for greater than five data points we can also determine the GLM and for ten or more points we can generate the GAM. Depending on how many data points we have and which series are included, we can then calculate this Average.


Demographic Index covariate

Baselines and projections for the 40 GHO indicators as well as the additional 9 UHC are either determined by the technical programmes or by the DDI. To build a systematic approach to infilling and extrapolating estimates for all countries we can use time as the main predictor as well as other social and demographic indices. A composite covariate developed for this report is a version of the Socio-demographic Index (SDI) that was originally developed for the 2015 Global Burden of Disease study. This development status indicator has components that are strongly correlated with health outcomes. It is the geometric mean of normalised 0 to 1 indices of total fertility rate, mean years of schooling for those aged 15 and older, and lag distributed income per capita. Instead of using the GBD2017 SDI (some of the input data for this measure are not publically available e.g. fertility), we construct the metric using publicly available fertility data from WPP2019 as well as GDP per capita and education data from the UN Human Development Reports. In the plot below, we compare this constructed SDI to the one from the GBD.

In general, there is strong agreement, between the SDI measure from the public data vs the one from the GBD. There are some missing values for a few countries in the public data and these can be imputed using a spline log-linear model that predicts the sdi.pub using the sdi.gbd and time. A summary of the regression results for such a model is shown below.

  sdi.pub
Predictors Estimates CI p
(Intercept) 0.54 0.54 – 0.54 <0.001
region [Europe & Central
Asia]
-0.00 -0.01 – 0.00 0.888
region [Latin America &
Caribbean]
0.05 0.05 – 0.06 <0.001
region [Middle East &
North Africa]
-0.02 -0.03 – -0.01 <0.001
region [North America] 0.03 0.02 – 0.05 <0.001
region [South Asia] -0.01 -0.02 – -0.00 0.010
region [Sub-Saharan
Africa]
-0.00 -0.01 – 0.00 0.199
Smooth term (sdi.gbd) 8.29 <0.001
Smooth term (lt) 1.42 0.218
Observations 3140
R2 0.968

Forecasting the SDI covariate

One of the main advantages of a covariate model is that it can be used to infill missing values in the retrospective data and once projected to the target year, it can be used to increase the robustness of any forecasts of the indicators. The SDI estimates for each country are forecasted using GAMS. Plots of the country and region specific SDI estimates and forecasts can be seen below.


– Summary assessment of indicators

SDG/WHA indicator no Indicator Family Summary Action
  1. SDG 1.5.1
Number of persons affected by disasters (per 100 000 population) HEP All countries but only has data for 2016 Update with GHE for baseline and projections (if any)
  1. SDG 1.a.2
Domestic general government health expenditure (GGHE-D) (% of general government expenditure (GGE)) UHC Robust time series to 2017 for almost all countries Can generate baselines/projections (or tech prog if available), country consultation if we generate
  1. SDG 2.2.1
Prevalence of stunting in children under 5 (%) HPOP Scattered data points, some countries with baselines To rely on tech progs and if they cannot supply, can estimate, country consultation required for some
  1. SDG 2.2.2
Prevalence of wasting in children under 5 (%) HPOP Scattered data points, some countries with baselines To rely on tech progs and if they cannot supply, can estimate, country consultation required for some
  1. SDG 2.2.2
Prevalence of overweight in children under 5 (%) HPOP Scattered data points, some countries with baselines To rely on tech progs and if they cannot supply, can estimate, country consultation required for some
  1. SDG 3.1.1
Maternal mortality ratio (per 100 000 live births) UHC Robust time series to 2017 for almost all countries Update with GHE for baseline and projections
  1. SDG 3.1.2
Proportion of births attended by skilled health personnel (%) UHC Scattered data points, some countries with baselines Can generate baselines/projections (or tech prog if available), country consultation if we generate
  1. SDG 3.2.1
Under-five mortality rate (per 1000 live births) UHC Robust time series to 2018 for all countries Update with GHE for projections
  1. SDG 3.2.2
Neonatal mortality rate (per 1000 live births) UHC Robust time series to 2018 for all countries Update with GHE for projections
  1. SDG 3.3.1
New HIV infections (per 1000 uninfected population) UHC Robust time series to 2019 for many countries (not 194 in database) To rely on tech programs for projections
  1. SDG 3.3.2
Tuberculosis incidence (per 100 000 population) UHC Robust time series to 2018 for all countries To rely on tech programs for projections
  1. SDG 3.3.3
Malaria incidence (per 1000 population at risk) UHC Robust time series to 2018 for relevant countries To rely on tech programs for projections
  1. SDG 3.3.4
Hepatitis B incidence (measured by: surface antigen (HBsAg) prevalence among children under 5 years) UHC All countries but only has data for 2015 Check tech program otherwise maybe look at Hep mortality as covariate, need to derive baselines and projections
  1. SDG 3.3.5
Number of people requiring interventions against NTDs UHC Robust time series to 2018 for all countries To rely on tech programs for projections??
  1. SDG 3.4.1
Probability of dying from any of CVD, cancer, diabetes, CRD (aged 30–70) (%) UHC Scattered data points in GHO but GHE will have time series and baselines Update with GHE for projections and baselines
  1. SDG 3.4.2
Suicide mortality rate (per 100 000 population) HPOP Scattered data points in GHO but GHE will have time series and baselines Update with GHE for projections and baselines
  1. SDG 3.5.1
Coverage of treatment interventions for substance-use disorders (%) UHC No data Ignore??
  1. SDG 3.5.2
Total alcohol per capita consumption in adults aged 15+ (litres of pure alcohol) HPOP Scattered data points, some countries with baselines To rely on tech progs and if they cannot supply, can estimate, country consultation required for some
  1. SDG 3.6.1
Road traffic mortality rate (per 100 000 population) HPOP All countries but only has data for 2016 Update with GHE for baseline and projections
  1. SDG 3.7.1
Proportion of women (aged 15–49) having need for family planning satisfied with modern methods (%) UHC Scattered data points, some countries with baselines To rely on tech progs and if they cannot supply, can estimate, country consultation required for some
  1. SDG 3.8.1
UHC Service Coverage Index UHC Composite of other indicators, generally using GLM or GAM for extrapolation Can generate baselines/projections (or tech prog if available), country consultation if we generate
  1. SDG 3.8.2
Population with household expenditures on health >10% of total household expenditure or income (%) UHC Scattered data points, some countries with baselines To rely on tech progs and if they cannot supply, can estimate, country consultation required for some
  1. SDG 3.9.1
Mortality rate attributed to air pollution (per 100 000 population) HPOP All countries but only has data for 2016 Update with GHE for baseline and projections
  1. SDG 3.9.2
Mortality rate attributed to exposure to unsafe WASH services (per 100 000 population) UHC All countries but only has data for 2016 Update with GHE for baseline and projections
  1. SDG 3.9.3
Mortality rate from unintentional poisoning (per 100 000 population) HPOP All countries but only has data for 2016 Update with GHE for baseline and projections
  1. SDG 3.a.1
Prevalence of tobacco use in adults aged 15+ (%) UHC & HPOP Not all countries but all in data base have 2018 To rely on tech progs and if they cannot supply, can estimate projection
  1. SDG 3.b.1
Proportion of population covered by all vaccines included in national programmes (DTP3, MCV2, PCV3) (%) UHC & HEP Robust time series to 2018 for all countries To rely on tech progs and if they cannot supply, can estimate projection
  1. SDG 3.b.3
Proportion of health facilities with essential medicines available and affordable on a sustainable basis (%) UHC Very small number of observations (countries and years) Can possibly model? But utility of indicator for 25 countries?
  1. SDG 3.c.1
Density of health workers (doctors; nurse and midwives; pharmacists; dentists per 10 000 population) UHC Robust time series to 2018 for most countries To rely on tech programs for projections and baselines for all countries
  1. SDG 3.d.1
International Health Regulations (IHR) capacity and health emergency preparedness HEP Almost all countries with data for 2018 and 2019 Will have to determine projections
  1. SDG 3.d.2
Proportion of bloodstream infections due to antimicrobial resistant organisms (%) UHC No data Ignore??
  1. SDG 4.2.1
Proportion of children under 5 developmentally on track (health, learning and psychosocial well-being) (%) HPOP Only subset of countries and subset of years, some max as far back as before 2010 Needs more data, was this from technical program? Need to find more data or to model
  1. SDG 5.2.1
Proportion of women (aged 15–49) subjected to violence by current or former intimate partner (%) HPOP Only subset of countries and subset of years, some max as far back as before 2011 Needs more data, was this from technical program? Need to find more data or to model
  1. SDG 5.6.1
Proportion of women (aged 15–49) who make their own decisions regarding sexual relations, contraceptive use and reproductive health care (%) HPOP Only subset of countries and subset of years, some max as far back as before 2012 Needs more data, was this from technical program? Need to find more data or to model
  1. SDG 6.1.1
Proportion of population using safely managed drinking water services (%) HPOP Robust time series to 2017 for about 90 countries Can generate baselines/projections (or tech prog if available), country consultation if we generate
  1. SDG 6.2.1
Proportion of population using safely managed sanitation services and hand-washing facility (%) HPOP Robust time series to 2017 for about 90 countries Can generate baselines/projections (or tech prog if available), country consultation if we generate
  1. SDG 7.1.2
Proportion of population with primary reliance on clean fuels (%) HPOP Robust time series to 2018 for most countries To rely on tech progs and if they cannot supply, can estimate projection
  1. SDG 11.6.2
Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (µg/m3) HPOP All countries but only has data for 2016 There is a model but no known projections, need to discuss with program. May have to level extrapolate.
  1. SDG 16.2.1
Proportion of children (aged 1–17) experiencing physical or psychological aggression (%) HPOP Need to get SDG data Data will be cross sectional so need to decide on rule of thumb for projection
  1. Health Emergencies (vaccines)
Vaccine coverage for epidemic prone diseases HEP No data Ignore??
  1. Health Emergencies (fragile)
Proportion of vulnerable people in fragile settings provided with essential health services (%) HEP No data Ignore??
  1. WHA66.10 (BP)
Prevalence of raised blood pressure in adults aged 18+ UHC Robust time series to 2015 for most countries To rely on tech programs for projections and baselines for all countries
  1. WHA66.10 (TFA)
Effective policy/regulation for industrially produced transfatty acids (TFA) (Y/N) HPOP Table for 2019 Assume 2019 values
  1. WHA66.10
Prevalence of obesity (%) HPOP Robust time series to 2016 for all countries Can generate baselines/projections (or tech prog if available), country consultation if we generate
  1. WHA68.3
Number of cases of poliomyelitis caused by wild poliovirus (WPV) HEP Number of countries with baselines Could just be assumed 0 I am sure, having been "“eliminated”"
  1. WHA68.7
Patterns of antibiotic consumption at national level UHC No data Ignore??
UHC 1 Family planning UHC Robust time series to 2019 for almost all countries GLM works well if technical program does not project
UHC 2 Suspected pneumo UHC Robust time series to 2016 forabout 120 countries A covariate model on U5MR exists and is supposed to work well. Can compare and rather use that. Consultation required for baselines
UHC 3 Population at risk of Malaria who slept under treated net UHC Pretty noisy but has baseline for endemic countries Potentially use average of linear and GAM for projection
UHC 4 Proportion of population using basic sanitation UHC Robust time series to 2016/17 for all countries GLM works well if technical program does not project
UHC 5 Proportion of TB cases that are treated UHC Robust time series to 2016/17 for all countries GLM works well if technical program does not project
UHC 6 Proportion of people living with HIV on ART UHC No all countries but necessary ones have baselines Will use tech programs baselines and projections for relevant countries
UHC 7 Mean fasting plasma glucose UHC For all countries and data up to 2014. Need to reassess utility of this indicator and if other can be found GLM works well enough but issue of country consultation of baselines
UHC 8 Number of Hospital beds available per 10,000 UHC Some countries have data to 2018 others do not Can use average or hierarchy of models (Fixed > Linear > GAM > GBoostM). Will require consultation for some
UHC 9 Proportion of wmen receiving antenatal care UHC Mixture, some have baselines and some do not Can use average or hierarchy of models (Fixed > Linear > GAM > GBoostM). Will require consultation for some

1. Number of persons affected by disasters (HEP)

Indicator SDG 1.5.1 is currently only available for 2016. It is, however, an output of the GHE and with the GHE update will be updated as well for the baseline with the GHE process covering country consultation. Projection is not straightforward. One could model disasters using a statistical distribution such as Dirichlet on historical data but this is not optimal. Using the previous proportions by countries seems to penalize countries that have experienced disasters and to reward those that have not. Consider projection usefulness by country. Perhaps project global/regional number of indicator and distribute it according to projected country-age-specific mortality, if at all. It is relevant to but not included in the HEP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 1.5.1
183 2016 2016 1 2016 2016 1 1 No 0

There are 0 countries with baselines


2. General Government Health expenditure (UHC)

Indicator SDG 1.a.2 is available for most years since the year 2000 with almost all countries having values for 2017. Using either linear extrapolation, GAMs or machine learning (GBoostM) algorithms, baselines and projections can be determined directly. Although the values predicted using the various methods are similar for many countries, in the extreme trend cases, the GBM approach seems to plausibly capture the latent effects beyond time - particularly when recent trends have been in a steep downward direction or the rate of increase for the most recent years is extremely/implausibly high. It is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 1.a.2
180 2000 2017 18 2017 2011 1 18 No 0


3. Prevalence of stunting in children under-5 (HPOP)

Indicator SDG 2.2.1 is based on survey data and is not available for many year-country combinations. With data for 145 countries summarized as shown in the table below, there are many missing data. The technical programs need to be consulted about 2018 and 2023 to follow up on modeled estimates they have produced, for which current status is currently unknown. We need to know if baselines and projections have been derived. This indicator is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 2.2.1
144 2002 2014 4 2014 2000 1 17 Yes 28

## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?


4. Prevalence of wasting in children under-5 (HPOP)

Indicator SDG 2.2.2 (wasting) is based on survey data and is not available for many year-country combinations. With data for 144 countries summarised as shown in the table below, there are many missing data. The technical programmes need to be consulted about 2018 and 2023 to follow up on modelled estimates they have produced, for which current status is currently unknown. We need to know if baselines and projections have been derived. This indicator is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 2.2.2
143 2002 2014 4 2015 2000 1 17 Yes 28

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5. Prevalence of overweight in children under-5 (HPOP)

Indicator SDG 2.2.2 (overweight) is based on survey data and is not available for many year-country combinations. With data for 143 countries summarised as shown in the table below, there are many missing data. The technical programmes need to be consulted about 2018 and 2023 to follow up on modelled estimates they have produced, for which current status is currently unknown. We need to know if baselines and projections have been derived. This indicator is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 2.2.2
142 2002 2014.5 4 2016 2000 1 17 Yes 28

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6. Maternal mortality ratio (per 100 000 live births) (UHC)

Indicator SDG 3.1.1 is available for all years since the year 2000 with all countries having values for 2017. Using either linear extrapolation, GAMs or machine learning (GBoostM) algorithms, baselines and projections can be determined directly. In most cases, the values predicted using the various methods are comparable. Updates for baselines or projections are expected as a part of the GHE update. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.1.1
183 2000 2017 18 2000 2017 18 18 No 0


7. Proportion of births attended by skilled health personnel (%) (HPOP)

Indicator SDG 3.1.2 is available for almost all countries but not all countries include a baseline. For some countries, there are sufficient points to use either linear extrapolation, GAMs or machine learning (GBoostM) algorithms. Accordingly, baselines and projections can be determined depending on the number of observations. More info is required on the technical programmes owning this indicator. This indicator is relevant to but not included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.1.2
176 2003 2017 8 2013 2006 1 19 Yes 59


8. Under-five mortality rate (per 1000 live births) (UHC)

Indicator SDG 3.2.1 is available for all countries and all countries include a baseline. For projections there are sufficient points to use either linear extrapolation, GAMs or machine learning (GBoostM) algorithms, however the technical programmes derive estimates. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.2.1
182 2000 2019 20 2000 2019 20 20 Yes 182


9. Neonatal mortality rate (per 1000 live births) (UHC)

Indicator SDG 3.2.2 is available for all countries and all countries include a baseline. For projections there are sufficient points to use either linear extrapolation, GAMs or machine learning (GBoostM) algorithms, however the technical programmes derive estimates. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.2.2
182 2000 2019 20 2000 2019 20 20 Yes 182


10. New HIV infections (per 1000 uninfected population) (UHC)

Indicator SDG 3.3.1 is available for a subset of countries for which some of them include a baseline. For projections there are sufficient points to use either linear extrapolation, GAMs or machine learning (GBoostM) algorithms, however the technical programmes derive estimates of both. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.3.1
117 2000 2019 20 2010 2014 10 20 Yes 114


11. Tuberculosis incidence (per 100 000 population) (UHC)

Indicator SDG 3.3.2 is available for all countries and includes a baseline. For projections there are sufficient points to use either linear extrapolation, GAMs or machine learning (GBoostM) algorithms, however the technical programmes derive estimates of both. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.3.2
183 2000 2018 19 2011 2018 8 19 Yes 183


12. Malaria incidence (per 1000 population at risk) (UHC)

Indicator SDG 3.3.3 is available for 107 countries and includes a baseline. For projections there are sufficient points to use either linear extrapolation, GAMs or machine learning (GBoostM) algorithms, however the technical programmes derive estimates of both. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.3.3
107 2000 2018 19 2000 2018 19 19 Yes 107


13. Hepatitis B incidence among children under 5 years (UHC)

Indicator SDG 3.3.4 is available for all countries but only for the year 2015, thus excluding a baseline. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.3.4
183 2015 2015 1 2015 2015 1 1 No 0

14. Number of people requiring interventions against NTDs (UHC)

Indicator SDG 3.3.5 is available for all countries since 2010 and the data includes baselines. For projections there are only a few data points to use, we apply the machine learning (GBoostM) algorithms, however the technical programmes derive estimates of both. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.3.5
183 2010 2018 9 2010 2018 9 9 Yes 183


15. Probability of dying from any of CVD, cancer, diabetes, CRD (aged 30–70) (%) (UHC)

Indicator SDG 3.4.1 is for all countries but for select years. Baselines and projections will be determined from the GHE. This indicator is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.4.1
183 2000 2016 5 2000 2016 5 5 No 0


16. Suicide mortality rate (per 100 000 population) (HPOP)

Indicator SDG 3.4.2 is currently available for all countries and for select years up to 2016. Baselines and projections will be determined from the GHE. This indicator is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.4.2
183 2000 2016 5 2000 2016 5 5 No 0


17. Coverage of treatment interventions for substance-use disorders (%) (UHC)

Not on the GHO and no data.


18. Total alcohol per capita consumption in adults aged 15+ (HPOP)

Indicator SDG 3.5.2 is currently available for all countries and for select years up to 2016 (need annual values). Baselines are available for all countries. The technical programmes (external to WHO) needs to be consulted about whether they have/can do projections, otherwise use average of defaults suggested here. This indicator is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.5.2
182 2000 2018 5 2015 2018 2 5 Yes 182


19. Road traffic mortality rate (per 100 000 population) (HPOP)

Indicator SDG 3.6.1 is currently only available for 2016 and for most countries. It is, however, an output of the GHE and with the GHE update will be updated as well for the baseline with the GHE process covering country consultation. It is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.6.1
172 2016 2016 1 2016 2016 1 1 No 0

20. Proportion of women having need for family planning satisfied (%) (UHC)

Indicator SDG 3.7.1 is based on survey data and is not available for many year-country combinations. With data for 126 countries summarised as shown in the table below, there are many missing data in GHO. According to UHC focal point, baseline and projections till 2030 already exist but not for all 194 countries. Clarify where the data are being kept. This indicator is included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.7.1
126 2005 2015 3 2016 2001 1 13 Yes 30


21. UHC Service Coverage Index (UHC)

Indicator SDG 3.8.1 is available for all countries but for only years 2015 and 2017. The ideal forecast approach would be to in-fill its constituent individual indices, determine aggregate and project the aggregate. It is included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.8.1
183 2015 2017 2 2015 2017 2 2 No 0


22. Population with household expenditures on health >10% of total household expenditure or income (%) (UHC)

Indicator SDG 3.8.2 is available for a subset of countries and not all years. Some have baselines. According to the focal point, the technical programme for this Financial protection indicator have projections for all countries and they need to validate them with World Bank, after which they would be available by October. Assume they would thus be running the necessary country consultation. If not available for dashboard, what interim values will be used for billions? Here we explore, the projections using the suite of models. It is included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.8.2
146 2003 2012 3 2016 2000 1 17 Yes 2


23. Mortality rate attributed to air pollution (per 100 000 population) (HPOP)

Indicator SDG SDG 3.9.1 is currently only available for 2016 and for all countries. It is, however, an output related to the GHE and with the GHE update will be updated as well for the baseline with the GHE process covering country consultation. It is relevant to but not included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.9.1
183 2016 2016 1 2016 2016 1 1 No 0

24. Mortality rate attributed to exposure to unsafe WASH services (per 100 000 population) (UHC)

Indicator SDG 3.9.2 is currently only available for 2016 and for all countries. It is, however, an output related to the GHE and with the GHE update will be updated as well for the baseline with the GHE process covering country consultation. It is relevant to but not included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.9.2
183 2016 2016 1 2016 2016 1 1 No 0

25. Mortality rate from unintentional poisoning (per 100 000 population) (HPOP)

Indicator SDG 3.9.3 is currently only available for 2016 and for all countries. It is, however, an output related to the GHE and with the GHE update will be updated as well for the baseline with the GHE process covering country consultation. It is relevant to but not included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.9.3
183 2000 2016 5 2000 2016 5 5 No 0

26. Prevalence of tobacco use in adults aged 15+ (UHC & HPOP)

Indicator SDG 3.a.1 is currently available for select countries and years up to 2018. It is used for both the HPOP and the UHC, with the former using crude and latter age-standardised rates. The methodology for infilling is currently being discussed with the technical programme. This indicator is included in the HPOP and UHC billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.a.1
144 2000 2018 9 2000 2018 9 9 Yes 144


27. Proportion of population covered by all vaccines included in national programmes (DTP3, MCV2, PCV3) (%) (HEP & UHC)

Indicator SDG 3.b.1 is currently available for all countries with all having baselines. It is used for both the HEP and the UHC, with the former being included in the billions calculation. Need to establish a methodology for projection and aggregation (extract baselines from WUENIC) and then summary statistic, perhaps min?. This indicator is included in the HEP billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.b.1
183 2000 2019 20 2011 2019 9 20 Yes 183


28. Proportion of health facilities with essential medicines available and affordable on a sustainable basis (%) (UHC)

IndicatorSDG 3.b.3 is currently only available for 25 countries. More data (and metadata) - including on source - are needed for this indicator to derive baselines and projections. It is relevant to but not included in the HEP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.b.3
25 2016 2016 1 2019 2008 1 4 No 0

29. Density of health workers per 10 000 population (UHC)

Indicator SDG 3.c.1 is currently available for all countries with most having baselines. The technical programme is currently producing outstanding baselines and projections but needs to confirm date of completion.. This indicator is included in the UHC billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.c.1
182 2001 2017 10.5 2016 2001 1 19 Yes 61


30. International Health Regulations (IHR) capacity and health emergency preparedness (UHC & HEP)

Indicator SDG 3.d.1 is currently available for all countries with most having baselines. However, there is no time-trend to use to generate a forecast/projection. From the plot, there appears to be a potential exponential relationship between the SDI and the logit of IHR. This indicator is included in the UHC and HEP billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 3.d.1
178 2018 2019 2 2019 2018 1 2 Yes 170

31. Proportion of bloodstream infections due to antimicrobial resistant organisms (%) (UHC)

Not on the GHO and no data.

32. Proportion of children under 5 developmentally on track (health, learning and psychosocial well-being) (%) (HPOP)

Indicator SDG 4.2.1 is currently available for only a subset of countries with none having baselines. Proposal is to just use the latest value for 2018 and mark that it is an earlier year. Question then extends to projections. Potentially could assume exponential relationship with SDI?. This indicator is included in the HPOP billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 4.2.1
60 2013 2013 1 2016 2009 1 1 No 0

33. Proportion of women (aged 15–49) subjected to violence by current or former intimate partner (%) (HPOP)

Indicator SDG 5.2.1 is currently available for only a subset of countries with none having baselines. Proposal is to just use the latest value for 2018 and mark that it is an earlier year. Question then extends to projections.. This indicator is included in the HPOP billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 5.2.1
71 2014 2014 1 2017 2000 1 1 No 0

34. Proportion of women (aged 15–49) who make their own decisions regarding sexual relations, contraceptive use and reproductive health care (%) (HPOP)

Indicator SDG 5.6.1 is currently available for only a subset of countries with none having baselines. Proposal is to just use the latest value for 2018 and mark that it is an earlier year. Question then extends to projections.. This indicator is not included in the HPOP billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 5.6.1
47 2010 2012 1 2016 2007 1 3 No 0

35. Proportion of population using safely managed drinking water services (%) (HPOP)

Indicator SDG 6.1.1 is available for about half the number of countries albeit none have baselines. According to the focal point, the technical programme for this WASH indicator has a model but no known projections, must discuss with program. If not available for dashboard, what interim values will be used for billions? Here we explore, the projections using the suite of models. It is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 6.1.1
94 2000 2017 18 2006 2017 12 18 No 0


36. Proportion of population using safely managed sanitation services and hand-washing facility (%) (HPOP)

Indicator SDG 6.2.1 is available for about half the number of countries albeit none have baselines. According to the focal point, the technical programme for this WASH indicator has a model but no known projections, must discuss with program. If not available for dashboard, what interim values will be used for billions? Here we explore, the projections using the suite of models. It is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 6.2.1
84 2000 2017 18 2006 2017 12 18 No 0


37. Proportion of population with primary reliance on clean fuels (%) (HPOP)

Indicator SDG 7.1.2 is available for all countries and has baselines. The technical programmes need to be consulted about projections. This indicator is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 7.1.2
179 2000 2018 19 2000 2018 19 19 Yes 179


38. Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (µg/m3) (HPOP)

Indicator SDG 11.6.2 is currently available for all countries but with none having baselines. There is a model but no known projections, need to discuss with program. Question then extends to value for baselines and projections if technical programs are not able to respond in time.. This indicator is included in the HPOP billions calculations.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. SDG 11.6.2
183 2016 2016 1 2016 2016 1 1 No 0

39. Proportion of children (aged 1–17) experiencing physical or psychological aggression (%) (HPOP)

Not on the GHO and no data.

40. Vaccine coverage for epidemic prone diseases (HEP)

Not on the GHO and no data.

41. Proportion of vulnerable people in fragile settings provided with essential health services (%) (HEP)

Not on the GHO and no data.

42. Prevalence of raised blood pressure in adults aged 18+ (UHC)

Indicator WHA66.10 is available for almost all countries but does not have baseline on GHO. However, preliminary baseline and projections available (need to be validated by technical programme).. This indicator is included in the UHC billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. WHA66.10 (BP)
181 2000 2015 16 2000 2015 16 16 No 0


43. Effective policy/regulation for industrially produced transfatty acids (TFA) (Y/N) (HPOP)

Country ISO3 Region Year Policy
Afghanistan AFG South Asia 2019 No
Angola AGO Sub-Saharan Africa 2019 don’t know
Albania ALB Europe & Central Asia 2019 No
United Arab Emirates ARE Middle East & North Africa 2019 Yes
Argentina ARG Latin America & Caribbean 2019 Yes
Armenia ARM Europe & Central Asia 2019 No
Antigua and Barbuda ATG Latin America & Caribbean 2019 No
Australia AUS East Asia & Pacific 2019 Yes
Austria AUT Europe & Central Asia 2019 Yes
Azerbaijan AZE Europe & Central Asia 2019 Yes
Burundi BDI Sub-Saharan Africa 2019 No
Belgium BEL Europe & Central Asia 2019 Yes
Benin BEN Sub-Saharan Africa 2019 No
Burkina Faso BFA Sub-Saharan Africa 2019 No
Bangladesh BGD South Asia 2019 No
Bulgaria BGR Europe & Central Asia 2019 Yes
Bahrain BHR Middle East & North Africa 2019 Yes
The Bahamas BHS Latin America & Caribbean 2019 No
Bosnia and Herzegovina BIH Europe & Central Asia 2019 No
Belarus BLR Europe & Central Asia 2019 Yes
Belize BLZ Latin America & Caribbean 2019 No
Bolivia BOL Latin America & Caribbean 2019 No
Brazil BRA Latin America & Caribbean 2019 No
Barbados BRB Latin America & Caribbean 2019 No
Brunei BRN East Asia & Pacific 2019 No
Bhutan BTN South Asia 2019 No
Botswana BWA Sub-Saharan Africa 2019 No
Central African Republic CAF Sub-Saharan Africa 2019 No
Canada CAN North America 2019 Yes
Switzerland CHE Europe & Central Asia 2019 Yes
Chile CHL Latin America & Caribbean 2019 Yes
China CHN East Asia & Pacific 2019 No
Cote d’Ivoire CIV Sub-Saharan Africa 2019 No
Cameroon CMR Sub-Saharan Africa 2019 No
Democratic Republic of the Congo COD Sub-Saharan Africa 2019 no response
Congo COG Sub-Saharan Africa 2019 No
Colombia COL Latin America & Caribbean 2019 Yes
Comoros COM Sub-Saharan Africa 2019 No
Cape Verde CPV Sub-Saharan Africa 2019 No
Costa Rica CRI Latin America & Caribbean 2019 No
Cuba CUB Latin America & Caribbean 2019 No
Cyprus CYP Europe & Central Asia 2019 Yes
Czech Republic CZE Europe & Central Asia 2019 Yes
Germany DEU Europe & Central Asia 2019 Yes
Djibouti DJI Middle East & North Africa 2019 No
Denmark DNK Europe & Central Asia 2019 Yes
Dominican Republic DOM Latin America & Caribbean 2019 No
Algeria DZA Middle East & North Africa 2019 No
Ecuador ECU Latin America & Caribbean 2019 No
Egypt EGY Middle East & North Africa 2019 No
Eritrea ERI Sub-Saharan Africa 2019 No
Spain ESP Europe & Central Asia 2019 Yes
Estonia EST Europe & Central Asia 2019 Yes
Ethiopia ETH Sub-Saharan Africa 2019 No
Finland FIN Europe & Central Asia 2019 Yes
Fiji FJI East Asia & Pacific 2019 No
France FRA Europe & Central Asia 2019 Yes
Federated States of Micronesia FSM East Asia & Pacific 2019 No
Gabon GAB Sub-Saharan Africa 2019 No
United Kingdom GBR Europe & Central Asia 2019 Yes
Georgia GEO Europe & Central Asia 2019 No
Ghana GHA Sub-Saharan Africa 2019 No
Guinea GIN Sub-Saharan Africa 2019 No
The Gambia GMB Sub-Saharan Africa 2019 No
Guinea-Bissau GNB Sub-Saharan Africa 2019 No
Equatorial Guinea GNQ Sub-Saharan Africa 2019 No
Greece GRC Europe & Central Asia 2019 Yes
Grenada GRD Latin America & Caribbean 2019 No
Guatemala GTM Latin America & Caribbean 2019 No
Guyana GUY Latin America & Caribbean 2019 No
Honduras HND Latin America & Caribbean 2019 No
Croatia HRV Europe & Central Asia 2019 Yes
Haiti HTI Latin America & Caribbean 2019 No
Hungary HUN Europe & Central Asia 2019 Yes
Indonesia IDN East Asia & Pacific 2019 No
India IND South Asia 2019 Yes
Ireland IRL Europe & Central Asia 2019 Yes
Iran IRN Middle East & North Africa 2019 Yes
Iraq IRQ Middle East & North Africa 2019 Yes
Iceland ISL Europe & Central Asia 2019 Yes
Israel ISR Middle East & North Africa 2019 Yes
Italy ITA Europe & Central Asia 2019 Yes
Jamaica JAM Latin America & Caribbean 2019 Yes
Jordan JOR Middle East & North Africa 2019 No
Japan JPN East Asia & Pacific 2019 No
Kazakhstan KAZ Europe & Central Asia 2019 Yes
Kenya KEN Sub-Saharan Africa 2019 No
Kyrgyzstan KGZ Europe & Central Asia 2019 Yes
Cambodia KHM East Asia & Pacific 2019 No
Kiribati KIR East Asia & Pacific 2019 No
South Korea KOR East Asia & Pacific 2019 No
Kuwait KWT Middle East & North Africa 2019 Yes
Laos LAO East Asia & Pacific 2019 No
Lebanon LBN Middle East & North Africa 2019 No
Liberia LBR Sub-Saharan Africa 2019 No
Libya LBY Middle East & North Africa 2019 No
Saint Lucia LCA Latin America & Caribbean 2019 No
Sri Lanka LKA South Asia 2019 No
Lesotho LSO Sub-Saharan Africa 2019 No
Lithuania LTU Europe & Central Asia 2019 Yes
Luxembourg LUX Europe & Central Asia 2019 Yes
Latvia LVA Europe & Central Asia 2019 Yes
Morocco MAR Middle East & North Africa 2019 Yes
Moldova MDA Europe & Central Asia 2019 Yes
Madagascar MDG Sub-Saharan Africa 2019 No
Maldives MDV South Asia 2019 No
Mexico MEX Latin America & Caribbean 2019 Yes
Macedonia MKD Europe & Central Asia 2019 No
Mali MLI Sub-Saharan Africa 2019 No
Malta MLT Middle East & North Africa 2019 Yes
Myanmar MMR East Asia & Pacific 2019 No
Montenegro MNE Europe & Central Asia 2019 No
Mongolia MNG East Asia & Pacific 2019 Yes
Mozambique MOZ Sub-Saharan Africa 2019 No
Mauritania MRT Sub-Saharan Africa 2019 No
Mauritius MUS Sub-Saharan Africa 2019 No
Malawi MWI Sub-Saharan Africa 2019 don’t know
Malaysia MYS East Asia & Pacific 2019 No
Namibia NAM Sub-Saharan Africa 2019 No
Niger NER Sub-Saharan Africa 2019 No
Nigeria NGA Sub-Saharan Africa 2019 No
Nicaragua NIC Latin America & Caribbean 2019 No
Netherlands NLD Europe & Central Asia 2019 Yes
Norway NOR Europe & Central Asia 2019 Yes
Nepal NPL South Asia 2019 No
New Zealand NZL East Asia & Pacific 2019 No
Oman OMN Middle East & North Africa 2019 Yes
Pakistan PAK South Asia 2019 No
Panama PAN Latin America & Caribbean 2019 No
Peru PER Latin America & Caribbean 2019 Yes
Philippines PHL East Asia & Pacific 2019 No
Papua New Guinea PNG East Asia & Pacific 2019 No
Poland POL Europe & Central Asia 2019 Yes
North Korea PRK East Asia & Pacific 2019 No
Portugal PRT Europe & Central Asia 2019 Yes
Paraguay PRY Latin America & Caribbean 2019 No
Qatar QAT Middle East & North Africa 2019 Yes
Romania ROU Europe & Central Asia 2019 Yes
Russian Federation RUS Europe & Central Asia 2019 Yes
Rwanda RWA Sub-Saharan Africa 2019 No
Saudi Arabia SAU Middle East & North Africa 2019 Yes
Sudan SDN Sub-Saharan Africa 2019 No
Senegal SEN Sub-Saharan Africa 2019 No
Singapore SGP East Asia & Pacific 2019 Yes
Solomon Islands SLB East Asia & Pacific 2019 No
Sierra Leone SLE Sub-Saharan Africa 2019 No
El Salvador SLV Latin America & Caribbean 2019 No
Somalia SOM Sub-Saharan Africa 2019 No
Serbia SRB Europe & Central Asia 2019 No
South Sudan SSD Sub-Saharan Africa 2019 No
Sao Tome and Principe STP Sub-Saharan Africa 2019 No
Suriname SUR Latin America & Caribbean 2019 No
Slovakia SVK Europe & Central Asia 2019 Yes
Slovenia SVN Europe & Central Asia 2019 Yes
Sweden SWE Europe & Central Asia 2019 Yes
Swaziland SWZ Sub-Saharan Africa 2019 No
Seychelles SYC Sub-Saharan Africa 2019 No
Syria SYR Middle East & North Africa 2019 No
Chad TCD Sub-Saharan Africa 2019 No
Togo TGO Sub-Saharan Africa 2019 No
Thailand THA East Asia & Pacific 2019 No
Tajikistan TJK Europe & Central Asia 2019 No
Turkmenistan TKM Europe & Central Asia 2019 Yes
Timor-Leste TLS East Asia & Pacific 2019 Yes
Tonga TON East Asia & Pacific 2019 No
Trinidad and Tobago TTO Latin America & Caribbean 2019 No
Tunisia TUN Middle East & North Africa 2019 Yes
Turkey TUR Europe & Central Asia 2019 Yes
Tanzania TZA Sub-Saharan Africa 2019 don’t know
Uganda UGA Sub-Saharan Africa 2019 No
Ukraine UKR Europe & Central Asia 2019 No
Uruguay URY Latin America & Caribbean 2019 No
United States USA North America 2019 Yes
Uzbekistan UZB Europe & Central Asia 2019 No
Saint Vincent and the Grenadines VCT Latin America & Caribbean 2019 No
Venezuela VEN Latin America & Caribbean 2019 No
Vietnam VNM East Asia & Pacific 2019 No
Vanuatu VUT East Asia & Pacific 2019 No
Samoa WSM East Asia & Pacific 2019 No
Yemen YEM Middle East & North Africa 2019 No
South Africa ZAF Sub-Saharan Africa 2019 No
Zambia ZMB Sub-Saharan Africa 2019 No
Zimbabwe ZWE Sub-Saharan Africa 2019 No

the existing data out to 2023. NB this is not modelled this is “expected”. It’s a policy indicator (Y/N)

44. Prevalence of obesity (%) (HPOP)

Indicator WHA66.10 is available for almost all countries but does not have baseline on GHO. The data are modelled, out of date, and the program has not responded, possible defaults are shown here.. This indicator is included in the HPOP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. WHA66.10
181 2000 2016 17 2000 2016 17 17 No 0


45. Number of cases of poliomyelitis caused by wild poliovirus (WPV) (HEP)

Indicator WHA68.3 is available for almost all countries with some having baseline on GHO. This indicator is not included in the HEP billions calculation.

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
  1. WHA68.3
183 2000 2018 19 2007 2010 9 19 Yes 154


46. Patterns of antibiotic consumption at national level (UHC)

Not on the GHO and no data.

Additional UHC billions indicators


Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
Universal Health Coverage 177 2000 2019 20 2000 2019 20 20 Yes 177


Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
NA 0 NA NA NA -Inf Inf Inf -Inf No 0

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
SDG 3.8.1 121 2002 2014 3 2016 2000 1 10 Yes 18

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
Universal Health Coverage 177 2000 2019 20 2000 2019 20 20 Yes 177

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
Universal Health Coverage 177 2000 2019 20 2000 2019 20 20 Yes 177

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
Universal Health Coverage 177 2000 2019 20 2000 2019 20 20 Yes 177

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
Universal Health Coverage 177 2000 2019 20 2000 2019 20 20 Yes 177

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
Universal Health Coverage 177 2000 2019 20 2000 2019 20 20 Yes 177

Indicator N countries Median min Median max Median nobs Max of min Min of max Min nobs Max nobs Incl 2018 N 2018
Universal Health Coverage 177 2000 2019 20 2000 2019 20 20 Yes 177